Analytics & Measurement

The Measurement Gap

Here’s a pattern we see repeatedly: a company deploys AI, it seems to be working, people say they like it, and then six months later someone asks “what’s the actual ROI?” and nobody can answer. The AI keeps running, but without clear measurement, it’s impossible to optimize, justify expansion, or catch problems before they compound.

The measurement gap isn’t a technology problem. It’s a planning problem. Most teams focus on building and deploying—understandably—and treat measurement as something they’ll figure out later. Later rarely comes, and when it does, the data they need was never collected.

We close that gap. Whether you’re instrumenting an existing system or setting up measurement for a new deployment, we build the frameworks, dashboards, and processes that turn “I think it’s working” into “here’s exactly what it’s doing, what it’s costing, and what it’s worth.”

Our Measurement Framework

We organize AI metrics into four categories, each answering a different business question:

Adoption. Is anyone actually using it? We track active users, usage frequency, feature utilization, and abandonment patterns. High adoption with low repeat usage means something different than low adoption with high satisfaction—and each requires a different response.

Quality. Is it doing a good job? This includes automated metrics (accuracy scores, relevance ratings, task completion rates) and sampled human evaluation. We build quality scoring that’s specific to your use case—support accuracy requires different evaluation than document summarization or data extraction.

Efficiency. Is it saving time and money? We measure processing time, cost per unit of work, human intervention rates, and error frequencies. These metrics connect directly to the business case and are the foundation for ROI calculations.

Health. Is it still working as well as it was? We track model drift, latency trends, error rate changes, and cost trajectory. These operational metrics provide early warning when something is degrading—before users notice and complain.

Building Dashboards People Actually Use

The fastest way to waste a measurement investment is to build a dashboard nobody looks at. We’ve seen plenty of organizations with comprehensive monitoring that no one checks until something breaks publicly.

Our approach is to build role-specific views. The executive dashboard shows ROI trends, initiative health, and cost trajectory—information for portfolio decisions. The operations dashboard shows quality scores, escalation patterns, and volume trends—information for day-to-day management. The engineering dashboard shows latency, errors, and system health—information for technical optimization.

Each view is designed around the decisions its audience makes. We include alerting so the dashboards work for you even when you’re not looking at them. And we build automated reports—weekly and monthly summaries that go to the right people without anyone having to generate them manually.

What Good Measurement Enables

With solid measurement in place, you can do things that are impossible without it: compare the cost-effectiveness of different AI approaches, identify which use cases deserve more investment, catch quality degradation in days instead of months, and present clear ROI data to the people who control budgets.

Measurement also changes how teams think about AI work. When you can see the impact of changes in near-real-time, iteration becomes faster and more confident. You stop guessing about what’s working and start knowing.